Complex wind field numerical wind tunnel simulation model and simulation method thereof

Abstract

The invention relates to the technical field of wind field wind tunnel simulation testing, discloses a complex wind field numerical wind tunnel simulation model and a simulation method thereof, and aims to accurately reproduce a low-altitude complex wind field and adapt to low-altitude aircraft testing. The method comprises the following steps: firstly, acquiring key geometric parameters of a wind tunnel and fan performance parameters, and constructing a numerical wind tunnel three-dimensional model; simplifying the model, naming the boundary and building a closed CFD fluid model; then dividing a surface grid in a layered manner, carrying out gradient encryption on a key region, and starting volume grid self-adaptive encryption; dividing a plurality of fluid domains, setting an interface, and configuring boundary conditions according to a target wind field; then solving parameters are set, a K-Omega model is used in a steady state, and a Sparar-Allmaras model is used in a transient state; and finally, solving is accelerated by using parallel / GPU, and a script is called for automatic post-processing. According to the scheme, wind field reproduction precision and calculation efficiency are both considered, and low-altitude aircraft pneumatic analysis can be supported.

Description

Technical Field

[0001] This invention relates to the field of wind tunnel simulation and testing technology, specifically to a numerical wind tunnel simulation model for complex wind fields and its simulation method. Background Technology

[0002] The low-altitude economy, a new economic form relying on low-altitude airspace within 1,000 meters and dominated by the general aviation industry, involves various industries such as low-altitude flight, air tourism, regional passenger transport, general aviation services, scientific research and education. It has become a core engine for global industrial upgrading and regional economic growth. The carriers of the low-altitude economy include manned and unmanned aircraft, such as unmanned aircraft, eVTOL (human / cargo), flying cars, and helicopters.

[0003] The flight safety of low-altitude aircraft is highly dependent on their adaptability to the complex wind field environment of the troposphere. The complex wind field environment in this region (such as gusts, wind shear, and continuous wind) can directly lead to attitude deviation, lift fluctuations, or even stall and crash of the aircraft. Therefore, accurately simulating complex wind fields and verifying the aerodynamic performance of aircraft has become a key technical support for the development of low-altitude economic and safe standards.

[0004] Traditional wind tunnel testing technologies (such as automotive wind tunnels and aviation wind tunnels) can no longer meet the complex wind field testing needs of low-altitude aircraft. On the one hand, automotive wind tunnels focus on the analysis of air resistance and aerodynamic noise of vehicles in closed flow fields. Their wind field types are limited, mostly horizontal and uniform, and the test space cannot be adapted to simulate multiple operating conditions such as vertical take-off and landing and hovering of aircraft. On the other hand, although traditional aviation wind tunnels can simulate high-altitude, high-speed airflow, they lack the ability to reproduce complex wind fields within 1,000 meters at low altitudes. Furthermore, the construction cost of physical wind tunnels is high (the cost of a single full-size wind tunnel exceeds 100 million yuan) and the testing cycle is long (a single test can take several weeks), making it difficult to support the rapid iteration R&D needs of low-altitude aircraft.

[0005] Existing numerical wind tunnel simulation technologies also have significant limitations. The multiphysics coupled environment simulation device (publication number CN118549078A), while simulating complex wind fields using a combination of horizontal, vertical, and rotating flow fans, focuses on the design of the physical device and does not involve CFD simulation methods for numerical wind tunnels. Furthermore, it does not optimize the wind tunnel geometry and flow field control strategies for the testing needs of low-altitude aircraft. The multi-environment simulation wind field test equipment (publication number CN120685286A), while improving space utilization through an axisymmetric structure and deployable roller blinds, remains limited to the wind field simulation logic of physical devices and has not established core technical systems such as mesh generation, turbulence model adaptation, and automatic post-processing at the numerical simulation level. The numerical wind tunnel simulation method based on a full-size automotive wind tunnel (publication number CN118036508A), while constructing a car-specific numerical simulation process, only addresses the horizontal wind field testing needs of automobiles and cannot reproduce the complex wind fields required by low-altitude aircraft, failing to consider the coupling effect between the aircraft and the wind field.

[0006] In summary, the current technology system has not yet formed a dedicated numerical wind tunnel simulation method for complex low-altitude wind fields, which makes it difficult to meet the integrated needs of aerodynamic design, engineering verification and airworthiness certification support for low-altitude aircraft. There is an urgent need to build a high-precision and high-efficiency numerical simulation solution covering the entire process in order to break through the limitations of traditional technologies and help the low-altitude economy industry to be safely implemented. Summary of the Invention

[0007] The present invention aims to provide a numerical wind tunnel simulation model and simulation method for complex wind fields, in order to solve the problems that traditional wind tunnel or numerical simulation methods in the prior art cannot accurately and efficiently reproduce the flow characteristics of complex wind fields at low altitudes, and are difficult to adapt to the multi-condition testing requirements of low-altitude aircraft.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A numerical wind tunnel simulation model and simulation method for complex wind fields, comprising: S1, acquire the key geometric parameters of the wind tunnel for the complex wind field and the key performance parameters of the wind turbine. The key geometric parameters include the three-dimensional geometric data of the outer nacelle, inner nacelle, vertical flow fan array, rotary flow fan, and rectifier grid. The key performance parameters of the wind turbine include the PQ curve, power curve, speed, and impeller diameter. Based on the aforementioned key geometric parameters, a three-dimensional numerical wind tunnel model based on the size characteristics of a complex wind tunnel in a solid entity is constructed. S2, perform geometric simplification and boundary naming on the numerical wind tunnel 3D model, retaining the outer cabin boundary, outer cabin ground boundary, inner cabin boundary, inner cabin ground boundary, inner cabin top boundary, vertical flow fan boundary, rotary flow fan boundary, inner cabin airflow outlet boundary, and rectifier grid boundary, and build a closed CFD fluid model; S3, perform mesh generation on the CFD fluid model. The mesh generation method includes: the surface mesh and volume mesh size of the outer cabin area are 1 / 20-1 / 100 of the solid wind tunnel size; the inner cabin and rectifier grid area are 1 / 500-1 / 1250 of the solid wind tunnel size; the top boundary of the inner cabin is 1 / 100-1 / 500 of the solid wind tunnel size; gradient densification zones are set for the vertical flow fan inlet and outlet, flow field shear layer, ground boundary layer, and effective flow field section; multi-layer boundary layer mesh is set for the wall area; and volume mesh adaptive densification technology is enabled to dynamically adjust the mesh resolution. S4, the CFD fluid model is divided into the outer cabin fluid domain, the inner cabin fluid domain, the inner cabin top fluid domain, the vertical flow fan array fluid domain, and the rotary flow fan fluid domain. An interface is set between adjacent fluid domains to realize the transmission of fluid information. Boundary conditions are configured according to the target wind field type. S5, set the solution parameters. The steady-state calculation uses the K-Omega turbulence model, and the transient calculation uses the Spalar-Allmaras separated vortex model or other LES sub-models. The fluid medium is constant density and incompressible air. The single-step time of the transient calculation is defined as 1-10° of fan rotation. The total physical time is not less than the main circulating airflow in the fluid domain circulates twice in the numerical wind tunnel. S6 enables parallel computing technology or GPU hardware acceleration for simulation and solves the problem. After the simulation is completed, it calls a preset Java or Python script for automatic post-processing.

[0009] The principles and advantages of this scheme are as follows: In practical applications, through multi-domain fluid models, hierarchical control of array fans, and various wind field boundary condition settings, it can reproduce various complex wind fields in the lower atmosphere under numerical conditions, overcoming the limitation of traditional wind tunnels that can only simulate a single wind field; through hierarchical mesh generation combined with multi-layer boundary layer meshes on the walls and adaptive density technology of the volume mesh, it can accurately capture micro-flow phenomena such as airflow shear layers and eddy shedding, and dynamically adjust the mesh resolution to avoid resource waste; parallel computing / GPU acceleration further shortens the large-scale simulation time, balancing accuracy and efficiency; the model structure is divided into multiple fluid domains, which can be flexibly expanded... For the same test object, by integrating a low-altitude aircraft simulation model into the numerical wind tunnel model and setting dedicated boundaries and monitoring parameters, it can synchronously simulate the dynamic effects of wind fields on aircraft, realizing coupled testing of "wind field-aircraft" without the need to build a separate aircraft simulation scenario; from geometric simplification and boundary naming to automatic post-processing, a standardized process is formed to reduce human intervention errors; the output images, data and reports can be directly used for aircraft aerodynamic optimization and airworthiness certification support, improving the efficiency of result conversion; this solution can also support the simulation of complex wind field types with different wind strengths, and can meet the aerodynamic characteristic analysis needs of different aircraft, with strong adaptability.

[0010] Preferably, as an improvement, the outer cabin is a polygonal outer cabin, the circumference of the polygonal outer cabin is composed of no less than eight vertical surfaces, the circumference is closed, the top of the polygonal outer cabin is a dome structure, the dome intersects with the eight vertical surfaces, and the bottom surface is a plane; The inner cabin is a polygonal inner cabin. The circumference of the polygonal inner cabin is composed of no less than eight vertical faces. The circumference is closed, and at least one face is used to install a vertical flow fan array. One face is used to install a flow rectifier grille. The top of the polygonal inner cabin is flat and has a circular opening. The opening area leaves space to arrange a rotating flow fan. Above the top of the polygonal inner cabin, there is also a cubic area with four circular openings around its perimeter to ensure that the airflow in the inner cabin is connected to the airflow in the outer cabin. The polygonal inner cabin is entirely enclosed within a polygonal outer cabin, allowing airflow between the inner and outer cabins to circulate freely through openings. The polygonal inner cabin enables low-altitude aircraft to simulate fixed, vertical take-off and landing, or relative airflow flight states within its space in both horizontal and vertical directions.

[0011] Technical Benefits: The optimized wind tunnel geometry, achieved through multi-faceted vertical surfaces and a closed structure, better simulates the flight state and airflow interaction of low-altitude aircraft, more closely resembling the wind field environment during actual flight. This supports different flight modes and enhances the engineering reference value of the simulation results. Simultaneously, ample horizontal and vertical space within the inner cabin meets the multi-condition simulation requirements of low-altitude aircraft, allowing for seamless switching of test scenarios without frequent adjustments to the wind tunnel structure, thus improving testing flexibility. The free flow between the inner and outer cabins via openings ensures stable airflow circulation along a pre-defined path, minimizing momentum loss during airflow circulation and effectively improving simulation efficiency.

[0012] Preferably, as an improvement, the key geometric parameters are obtained through on-site measurement in a physical wind tunnel, parameter extraction from design drawings, or 3D reconstruction of wind tunnel images. The key geometric parameters are input in CATIA or STP file format, and the performance parameters are input in Excel or CSV file format.

[0013] Technical benefits: By acquiring parameters through multiple channels, different scenarios can be covered, ensuring that the geometric dimensions and wind turbine performance parameters of the numerical wind tunnel model match those of the physical wind tunnel, avoiding simulation deviations caused by missing parameters; the standard input format of geometric and performance parameters is clearly defined, adapting to the data reading requirements of mainstream simulation software without the need for additional format conversion, thus improving the operability and universality of the solution.

[0014] Preferably, as an improvement, the mesh generation method is applicable to numerical simulation software that solves the Navier-Stokes equations based on the finite volume method and numerical simulation software that solves the Boltzmann discrete equations based on the lattice Boltzmann method.

[0015] Technical benefits: It is compatible with both conventional simulation software based on the finite volume method and high-resolution software based on the lattice Boltzmann method (LBM), eliminating the need to repeatedly develop mesh models for different computational frameworks; the finite volume method is suitable for engineering-grade conventional precision simulation, while LBM is suitable for transient high-resolution simulation. The compatibility of the two methods can meet the needs of the entire process from the early stage of R&D to refined verification, improving the technical scalability of the solution.

[0016] Preferably, as an improvement, the volume mesh adaptive densification technology includes: identifying areas that need densification based on the wind field velocity gradient, turbulence intensity, and the flow field characteristics of the interaction between the aircraft and the wind field; dynamically adjusting the mesh density of the identified areas based on the airflow region; and automatically optimizing the mesh division based on error estimation or gradient analysis methods.

[0017] Technical benefits: By identifying encrypted regions, it is easy to automatically improve the mesh resolution in key areas, ensuring the simulation accuracy of key areas; the encryption strategy is adjusted for airflow regions to avoid the waste of computing resources caused by uniform encryption of the entire region; the mesh is automatically optimized by combining error estimation / gradient analysis, which shortens the calculation time while ensuring accuracy.

[0018] Preferably, as an improvement, in S4, when there are no key geometric parameters of the fan, the fan is simplified to a cylindrical model and set as the fan boundary, and the rotational speed, PQ curve, blade angle and turbulence intensity are input; when there are key geometric parameters of the fan, the actual rotation mode is used to simulate and the rotational speed and turbulence intensity are input.

[0019] Technical benefits: Differentiated solutions are designed for scenarios with and without fan geometry parameters. When there are no parameters, the simulation is simplified to a cylindrical fan boundary to avoid simulation interruptions due to missing parameters. When there are parameters, a real rotation simulation is used to reproduce the disturbance of airflow caused by impeller rotation, adapting to the completeness of fan data at different stages of R&D. Regardless of the simulation method used, the performance parameters are synchronously input with the same as those of the actual fan to ensure that the wind speed and pressure distribution of the fan-driven wind field are consistent with those of the actual wind tunnel, avoiding wind field distortion caused by simplification of the fan simulation.

[0020] Preferably, as an improvement, the low-altitude aircraft includes, but is not limited to, eVTOL, unmanned aerial vehicles, light aircraft, flying cars, and helicopters.

[0021] Technical benefits: Clearly adaptable to low-altitude aircraft, implicitly optimizing boundary conditions and monitoring parameters for different aircraft characteristics, allowing switching of test objects without adjusting the core simulation framework; providing a unified simulation standard for airworthiness certification support and engineering verification of different types of aircraft, reducing redundant development of methods due to differences in aircraft types, and accelerating the process of low-altitude aircraft from research and development to industrialization.

[0022] Preferably, as an improvement, the inner cabin can integrate a low-altitude aircraft simulation model, set the rotation boundary, solid boundary, and cooling module boundary of the aircraft simulation model, locally refine the mesh around the aircraft, and simultaneously set monitoring parameters for flight attitude, force, and surface pressure.

[0023] Technical benefits: It facilitates the accurate capture of the dynamic interaction between the wind field and the aircraft, solving the problem that traditional wind tunnels can only simulate a single wind field and cannot be linked to aircraft testing.

[0024] Preferably, as an improvement, the automatic post-processing includes, but is not limited to: generating calculation result reports, spatial cross-sectional cloud maps, spatial isosurface cloud maps, and spatial streamline maps; wherein, the images are output in PNG or JPG format, and the monitoring parameters are output in CSV format.

[0025] Technical benefits: It facilitates the reduction of post-processing time and can adapt to various application scenarios. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the basic geometric state structure of a numerical wind tunnel for complex wind fields according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the basic boundary structure of a numerical wind tunnel for complex wind fields according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the state-one structure of a multi-vertical flow fan in a numerical wind tunnel for complex wind fields according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a multi-vertical flow fan in a numerical wind tunnel for complex wind fields according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a constant wind simulation flow field in a numerical wind tunnel for complex wind fields, according to an embodiment of the present invention. Figure 6 This is a schematic diagram of the vertical wind shear flow field in a numerical wind tunnel for complex wind fields, according to an embodiment of the present invention. Figure 7 This is a schematic diagram of the flow field inside the numerical wind tunnel with an unmanned aerial vehicle in a complex wind field, according to an embodiment of the present invention. The reference numerals in the accompanying drawings include: outer compartment 1, inner compartment 2, vertical flow fan array 3, rotary flow fan 4, flow straightener grille 5, outer compartment boundary 11, outer compartment ground boundary 12, inner compartment boundary 21, inner compartment ground boundary 22, inner compartment top boundary 23, inner compartment airflow outlet boundary 24, vertical flow fan boundary 31, rotary flow fan boundary 41, and flow straightener grille boundary 51. Detailed Implementation The following detailed description illustrates the specific implementation method: The basic implementation examples are as follows: Figure 1As shown, a numerical wind tunnel simulation model and method for complex wind fields are presented. By constructing a numerical wind tunnel simulation model based on a complex wind field wind tunnel, the model enables numerical simulation of complex wind fields such as constant winds, gusts, wind shear, and downbursts of different levels. This provides a simulation foundation for aerodynamic performance analysis and verification of low-altitude aircraft, eVTOL, unmanned aerial vehicles, flying cars, and helicopters. Specifically, the method includes: S1. Obtain the key geometric parameters of the wind tunnel for the complex wind field and the key performance parameters of the wind turbine. The key geometric parameters include the three-dimensional geometric data of the outer nacelle 1, inner nacelle 2, vertical flow fan array 3, rotary flow fan 4, and rectifier grid 5. The key performance parameters of the wind turbine include the PQ curve, power curve, speed, and impeller diameter.

[0027] The key geometric parameters are obtained through on-site measurements in a physical wind tunnel, parameter extraction from design drawings, or 3D reconstruction of wind tunnel images. The key geometric parameters are input in CATIA or STP file format, and the performance parameters are input in Excel or CSV file format.

[0028] A three-dimensional numerical wind tunnel model based on the key geometric parameters and the dimensional characteristics of a complex wind tunnel in a solid entity is constructed. The outer hull 1 is a polygonal outer hull, as shown below. Figure 1 As shown, the circumference of the polygonal outer cabin is composed of no fewer than eight vertical surfaces, and the circumference is closed. The top of the polygonal outer cabin is a dome structure, which intersects with the eight vertical surfaces, and the bottom surface is a plane. The inner cabin 2 is a polygonal inner cabin. The circumference of the polygonal inner cabin is composed of no fewer than eight vertical surfaces, and the circumference is closed. At least one surface is used to install a vertical flow fan array, and one surface is used to install a flow straightener. The top of the polygonal inner cabin is a plane with a circular opening, and the opening area reserves space to arrange a rotating flow fan. Above the top of the polygonal inner cabin, there is also a cubic area with four circular openings around its perimeter to ensure that the airflow in the inner cabin is connected to the airflow in the outer cabin. The polygonal inner cabin is completely enclosed in the polygonal outer cabin, and the airflow in the inner cabin and the outer cabin can flow freely through the opening area. The polygonal inner cabin has sufficient space in the horizontal and vertical directions to ensure that the low-altitude aircraft can simulate fixed, vertical take-off and landing, or relative airflow flight states within its space.

[0029] S2, perform geometric simplification and boundary naming on the numerical wind tunnel 3D model, such as... Figure 2 , 3 As shown in Figure 4, retain the outer cabin boundary 11, outer cabin ground boundary 12, inner cabin boundary 21, inner cabin ground boundary 22, inner cabin top boundary 23, vertical flow fan boundary 31 (several fans), rotating flow fan boundary 41 (several fans), inner cabin airflow outlet boundary 24, and rectifier grid boundary 51 to build a closed CFD fluid model.

[0030] S3. Mesh the CFD fluid model. The meshing method includes: for the outer compartment 1 region (outer compartment boundary 11 and outer compartment ground boundary 12), the surface and volume mesh sizes are 1 / 20 to 1 / 100 of the solid wind tunnel size, and the volume mesh refinement zone should completely cover the entire wind tunnel. For the inner compartment 2 (inner compartment boundary 21 and inner compartment ground boundary 22) and the flow straightening grid region, the surface and volume mesh sizes are 1 / 500 to 1 / 1250 of the solid wind tunnel size. Spatial refinement zones of various size gradients are set for key areas (vertical flow fan inlet and outlet, flow field shear layer, ground boundary layer, and effective flow field section). The surface and volume mesh sizes of the inner compartment top boundary 23 are 1 / 100 to 1 / 500 of the solid wind tunnel size. The meshing method is applicable to numerical simulation software that solves the Navier-Stokes equations based on the finite volume method and numerical simulation software that solves the Boltzmann discrete equations based on the lattice Boltzmann method.

[0031] Multiple boundary layer grids are set on the wall areas such as outer cabin boundary 11, outer cabin ground boundary 12, inner cabin boundary 21, inner cabin ground boundary 22, inner cabin top boundary 23, inner cabin airflow outlet boundary 24, and flow straightening grid boundary 51 to simulate the shear force of airflow on the wall, so as to more accurately capture phenomena such as airflow velocity gradient, flow separation, eddies, and eddy shedding near the wall.

[0032] In addition to setting spatial densification methods with size gradients in the volumetric mesh region of the fluid, adaptive volumetric mesh densification technology can be enabled to dynamically adjust the mesh resolution according to the complexity of the flow field and the needs of key areas. This adaptive volumetric mesh densification technology includes: identifying areas requiring densification based on wind velocity gradients, turbulence intensity, and the interaction characteristics between the aircraft and the wind field; dynamically adjusting the mesh density of the identified areas to improve simulation accuracy, especially in areas with drastic wind speed changes, turbulent regions, or airflow regions around the aircraft; and automatically optimizing the mesh generation based on error estimation or gradient analysis methods, enabling simulation calculations to improve computational efficiency while maintaining accuracy. Adaptive mesh densification also adjusts the mesh generation accuracy according to different wind field types (such as gusts, wind shear, downbursts, etc.).

[0033] S4, the CFD fluid model is divided into multiple fluid domains, including at least an outer cabin fluid domain, an inner cabin fluid domain, an inner cabin top fluid domain, a vertical flow fan array fluid domain, and a rotary flow fan fluid domain. An interface is set between adjacent fluid domains to form an internal closed flow environment to realize fluid information transmission.

[0034] Configure boundary conditions according to the target wind field type; the target wind field type includes, but is not limited to, constant wind, vertical wind shear, horizontal wind shear, gusts, and downbursts of different levels. Applicable low-altitude aircraft include, but are not limited to: eVTOL (electric vertical takeoff and landing aircraft), unmanned aerial vehicles, light aircraft, flying cars, helicopters, etc.

[0035] The simulation of complex wind fields references the actual wind field conditions during wind tunnel operation. If the CFD fluid model lacks specific geometric parameters for the fans, the fans (each fan in the vertical flow fan array and the rotating flow fan) are simplified to cylindrical models and set as fan boundaries. Fan boundary properties such as fan speed, PQ curve, and blade angle, consistent with actual wind tunnel operation, are added. The turbulence intensity at the fan inlet and outlet is also considered to accurately reproduce the complex wind field of the actual wind tunnel. If specific fan geometric parameters are available, the fans are simulated using actual rotation, with fan speeds consistent with actual wind tunnel operation, and the turbulence intensity at the fan inlet and outlet is also considered. The number of vertical flow fan arrays, the number of fans, and the arrangement of rotating flow fans are set based on the actual wind tunnel operation to achieve accurate reproduction of complex wind fields.

[0036] For example, to simulate constant wind at different wind speeds, the simulation model should include at least: an outer cabin fluid domain, an inner cabin fluid domain, an inner cabin top fluid domain, and a vertical flow fan array fluid domain. The outlet velocity of the vertical flow fans should be set according to the wind speed range in the standard GB / T 28591-2012 Wind Speed ​​Classification. Figure 5 This is a schematic diagram of a constant wind simulated flow field.

[0037] To simulate gusts of varying wind strengths, the simulation model should include at least the following: an outer cabin fluid domain, an inner cabin fluid domain, an inner cabin top fluid domain, and a vertical flow fan array fluid domain. The outlet velocity of the vertical flow fans should be set with reference to the wind speed range and corresponding gust time points in the standard GB / T 28591-2012 Wind Force Class, or by inputting relevant empirical formulas for gusts from the incoming atmospheric flow into the boundary conditions of the vertical flow fan outlet, or by using user-specified data tables, custom functions, etc., to simulate gusts.

[0038] To simulate vertical wind shear, the simulation model should include at least the following: an outer hull fluid domain, an inner hull fluid domain, an inner hull top fluid domain, and a vertical flow fan array fluid domain. The outlet of the vertical flow fan array should reference the actual wind shear vector velocity (e.g., mild wind shear: 4 knots / 30 meters (approximately 0.07 s⁻¹)), or a user-specified data table, custom function, etc. The wind speed of each horizontal fan in the array should be adjusted from bottom to top (or top to bottom) to achieve the desired wind shear vector velocity flow field; such as... Figure 6 Schematic diagram of vertical wind shear flow field.

[0039] To simulate horizontal wind shear, the simulation model should include at least the following: an outer cabin fluid domain, an inner cabin fluid domain, an inner cabin top fluid domain, and a vertical flow fan array fluid domain. The outlet of the vertical flow fan array should reference the actual wind shear vector velocity (e.g., for mild wind shear: 4 knots / 30 meters (approximately 0.07 s⁻¹)), or a user-specified data table, custom function, etc. The wind speed of each longitudinal fan in the array should be adjusted from left to right (or right to left) to achieve the desired wind shear vector velocity flow field. To simulate a downburst, the simulation model should include at least the following components: an outer compartment fluid domain, an inner compartment fluid domain, an inner compartment top fluid domain, and a rotating fan fluid domain. Since the downburst simulation has a wide impact on the surrounding flow field, at least four inner compartment airflow outlet boundaries should be included in the model. The downburst simulation should use a rotating fan (blower) positioned at the top of the inner compartment, with the airflow generated by the fan rotating downwards in the negative Z direction (blowing). The boundary conditions should be set based on the actual downburst wind force level, or using user-specified data tables, custom functions, etc. During simulation, the airflow path is as follows: airflow enters the inner compartment from the top of the rotating fan, exits the inner compartment through the four inner compartment airflow outlets, and returns to the top of the rotating fan from the outer compartment flow channel, forming a closed-loop flow.

[0040] S5, Set the solution parameters. When solving the Navier-Stokes equations using the finite volume method for steady-state (stationary) and transient (unsteady) conditions, the turbulence model should be set to the K-Omega model. When performing transient calculations based on the Navier-Stokes equations using the finite volume method, it is recommended to set the turbulence model to the Spalar-Allmaras separated eddy model or another LES sub-model. For the discrete form based on the Boltzmann equations, only transient solutions should be performed. The fluid medium is constant-density, incompressible air.

[0041] During steady-state calculations, the differences between the residual curves and user-defined monitored data (such as the average velocity at a cross section in the flow field, wind speed / pressure at a point, etc.) are examined to determine whether the calculation has converged. During transient calculations, to ensure solution accuracy and convergence, the time step of a single solution in the simulation analysis is defined according to the time step of the fan rotation in the model from 1 to 10°, and the number of internal iterations is determined with reference to the actual solution speed. To ensure flow field stability and the realism of transient simulations, the total physical duration of the simulation is not less than the circulation of the main circulating airflow in the fluid domain in the numerical wind tunnel twice.

[0042] When adding a low-altitude aircraft simulation model to the numerical wind tunnel cabin, the following settings are made for the aircraft simulation model: rotational boundaries (e.g., rotor rotation), solid boundaries (e.g., fuselage without slip), cooling module boundaries (e.g., motor cooling airflow), mesh model, and volume mesh refinement method. Monitoring parameters are simultaneously set during the simulation solution, such as... Figure 7 This is a schematic diagram of the flow field inside the cabin of an unmanned aerial vehicle.

[0043] S6 enables parallel computing technology or GPU hardware acceleration for simulation, thereby speeding up the calculation process and improving computational efficiency. After the simulation is completed, it calls a preset Java or Python script for automatic post-processing. Automatic post-processing includes, but is not limited to: generating calculation result reports, spatial cross-sectional cloud maps, spatial isosurface cloud maps, and spatial streamline maps; among which, images are output in PNG or JPG format, and monitoring parameters are output in CSV format.

[0044] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A numerical wind tunnel simulation model and simulation method for complex wind fields, characterized in that, include: S1, Obtain key geometric parameters of the wind tunnel for the complex wind field and key performance parameters of the wind turbine. The key geometric parameters include three-dimensional geometric data of the outer nacelle, inner nacelle, vertical flow fan array, rotary flow fan, and rectifier grid. The key performance parameters of the wind turbine include PQ curve, power curve, speed, and impeller diameter. Based on the aforementioned key geometric parameters, a three-dimensional numerical wind tunnel model based on the size characteristics of a complex wind tunnel in a solid entity is constructed. S2, perform geometric simplification and boundary naming on the numerical wind tunnel 3D model, retaining the outer cabin boundary, outer cabin ground boundary, inner cabin boundary, inner cabin ground boundary, inner cabin top boundary, vertical flow fan boundary, rotating flow fan boundary, inner cabin airflow outlet boundary, and rectifier grid boundary, and build a closed CFD fluid model; S3, perform mesh generation on the CFD fluid model. The mesh generation method includes: the surface mesh and volume mesh size of the outer cabin area are 1 / 20-1 / 100 of the solid wind tunnel size; the inner cabin and rectifier grid area are 1 / 500-1 / 1250 of the solid wind tunnel size; the top boundary of the inner cabin is 1 / 100-1 / 500 of the solid wind tunnel size; gradient densification zones are set for the vertical flow fan inlet and outlet, flow field shear layer, ground boundary layer, and effective flow field section; multi-layer boundary layer mesh is set for the wall area; and volume mesh adaptive densification technology is enabled to dynamically adjust the mesh resolution. S4, the CFD fluid model is divided into the outer cabin fluid domain, the inner cabin fluid domain, the inner cabin top fluid domain, the vertical flow fan array fluid domain, and the rotary flow fan fluid domain. An interface is set between adjacent fluid domains to realize the transmission of fluid information. Boundary conditions are configured according to the target wind field type. S5, set the solution parameters. The steady-state calculation uses the K-Omega turbulence model, and the transient calculation uses the Spalar-Allmaras separated vortex model or other LES sub-models. The fluid medium is constant density and incompressible air. The single-step time of the transient calculation is defined as 1-10° of fan rotation. The total physical time is not less than the airflow of the main circulating flow in the fluid domain circulates twice in the numerical wind tunnel. S6 enables parallel computing technology or GPU hardware acceleration for simulation and solves the problem. After the simulation is completed, it calls a preset Java or Python script for automatic post-processing.

2. The numerical wind tunnel simulation model and simulation method for complex wind fields according to claim 1, characterized in that: The outer cabin is a polygonal outer cabin, the circumference of which is composed of no less than eight vertical surfaces and the circumference is closed. The top of the polygonal outer cabin is a dome structure, the dome intersects with the eight vertical surfaces, and the bottom surface is a plane. The inner cabin is a polygonal inner cabin. The circumference of the polygonal inner cabin is composed of no less than eight vertical faces. The circumference is closed, and at least one face is used to install a vertical flow fan array. One face is used to install a flow rectifier grille. The top of the polygonal inner cabin is flat and has a circular opening. The opening area leaves space to arrange a rotating flow fan. Above the top of the polygonal inner cabin, there is also a cubic area with four circular openings around its perimeter to ensure that the airflow in the inner cabin is connected to the airflow in the outer cabin. The polygonal inner cabin is entirely enclosed within a polygonal outer cabin, allowing airflow between the inner and outer cabins to circulate freely through openings. The polygonal inner cabin enables low-altitude aircraft to simulate fixed, vertical take-off and landing, or relative airflow flight states within its space in both horizontal and vertical directions.

3. The numerical wind tunnel simulation model and simulation method for complex wind fields according to claim 1, characterized in that: The key geometric parameters are obtained through on-site measurements in a physical wind tunnel, parameter extraction from design drawings, or 3D reconstruction of wind tunnel images. The key geometric parameters are input in CATIA or STP file format, and the performance parameters are input in Excel or CSV file format.

4. The numerical wind tunnel simulation model and simulation method for complex wind fields according to claim 1, characterized in that: The mesh generation method described above is applicable to numerical simulation software that solves the Navier-Stokes equations based on the finite volume method and numerical simulation software that solves the Boltzmann discrete equations based on the lattice Boltzmann method.

5. The numerical wind tunnel simulation model and simulation method for complex wind fields according to claim 1, characterized in that, The volume mesh adaptive densification technology includes: identifying areas that need densification based on the wind field velocity gradient, turbulence intensity, and the flow field characteristics of the interaction between the aircraft and the wind field; dynamically adjusting the mesh density of the identified areas based on the airflow region; and automatically optimizing the mesh division based on error estimation or gradient analysis methods.

6. The numerical wind tunnel simulation model and simulation method for complex wind fields according to claim 1, characterized in that: In S4, when there are no key geometric parameters of the fan, the fan is simplified to a cylindrical model and set as the fan boundary, and the speed, PQ curve, blade angle and turbulence intensity are input; when there are key geometric parameters of the fan, the actual rotation mode is used to simulate and the speed and turbulence intensity are input.

7. The numerical wind tunnel simulation model and simulation method for complex wind fields according to claim 1, characterized in that: The low-altitude aircraft include, but are not limited to, eVTOLs, unmanned aerial vehicles, light aircraft, flying cars, and helicopters.

8. The numerical wind tunnel simulation model and simulation method for complex wind fields according to claim 1, characterized in that: The inner cabin can integrate a low-altitude aircraft simulation model, set the rotation boundary, solid boundary, and cooling module boundary of the aircraft simulation model, locally refine the mesh around the aircraft, and simultaneously set monitoring parameters for flight attitude, force, and surface pressure.

9. The numerical wind tunnel simulation model and simulation method for complex wind fields according to claim 1, characterized in that, The automatic post-processing includes, but is not limited to: generating calculation result reports, spatial cross-sectional cloud maps, spatial isosurface cloud maps, and spatial streamline maps; wherein, images are output in PNG or JPG format, and monitoring parameters are output in CSV format.

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